| abstract |
Many visualization problems arise from the ever increasing volume, complexity and dynamic nature of today's data sets, since most existing visualization metaphors and concepts do not scale well on such large data sets as interaction capabilities and visual representations suffer from the massive number of data points. In this context the aim of this research project is the development of a scalable visual analysis technique for temporal data sets and temporal data streams based on a tree-based, relevance driven approach that provides a compact representations of the data, and thus reduces the data volume in the visualization step. The basic idea is to provide the data at different granularities, based on the relevance of single data points or sets of them.
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